Global Internet traffic shows a very important growing trend. Global IP traffic has increased eightfold over the past 5 years, will increase fourfold over the next 5 years and there will be more and more available contents and information in the Internet, more particularly with the rise of the social networks activities or with the rise of user generated and crowdsourced contents (such as video contents). In the context of search engines, it is important to be able to structure and rank the relevancy of each content in order to retrieve the right information relevant to each search request.
An existing approach to solve this problem is known as PageRank. PageRank, performs only object-level ranking on homogeneous information objects. The basic idea of PageRank consists of qualitatively ranking a homogeneous graph formed by pointers between web pages by iteratively taking into account the relevancy of a node which points to another node from the exploration of the graph.
Other solutions are quantitative ranking, such as based on number of views or the ratio of the number of Likes for Youtube video content, or local graph analysis such as H-index or number of citations for research publications. Those solutions are insufficient in terms of quality evaluation.
The document “Co-ranking Authors and Documents in a Heterogeneous Network” by Ding Zhou et al. Seventh IEEE International Conference on Data Mining (2007) describes a method for co-ranking authors and their publication using several networks: the social network connecting authors, the citation network connecting the publications as well as the authorship network that ties the authors and the publications together. The co-ranking is based on equations calculating a probability distribution on all the authors and publications. More precisely, this document describes an algorithm which distributes probabilities in a deterministic way and which uses arbitrary factors so as to specify predetermined profiles of paths.
US2006/112392 describes a system for ranking messages of discussion threads based on relationships between messages and authors. The ranking system defines an equation for attributes of a message and an author. The equations define the attribute values and are based on relationships between the attribute and the attributes associated with the same type of object, and different types of objects. The ranking system iteratively calculates the attribute values for the objects using the equations until the attribute values converge on a solution. The ranking system then ranks the messages based on attribute values.
US2005/0165780 describes a method of organizing electronic document-related information. The method includes a step of generating a collection of electronic documents, a step of forming from the collection, at least one cluster of documents based upon a user's selection of a subject, and a step of determining for each author of documents in the cluster, the number of times each the author is an author of a document corresponding to the subject. The authors are ranked and presented to the user in the form of an index. The ranked index can be interpreted as a ranking of subject matter experts.
The document “Generalized comparison of graph-based ranking algorithms for publications and authors” by A. Sidiropoulos et al., The Journal of systems & software 79 (2006) analyses algorithms used for Link Analysis Ranking. This document further describes a ranking method designed for citation graphs.